I find where technology can help people, shape what it should be, and prove that it works.

Generative and evaluative research with jobs-to-be-done interviews, contextual inquiry, field shadowing, workshops, participatory design, and surveys. I've studied clinicians, nurses, integration engineers, data practitioners, and enterprise developers, often in technical domains I had to learn first, from HL7 and FHIR to speech grammars.

I build AI agents that extract meaningful excerpts and insights from raw data, and synthesis agents that support topic identification and shape actionable findings. I develop the data in stages, so even the lowest-level excerpt carries meaning and relationships. That makes it reusable for meta-analysis, for new inquiries, and for different top-level views: an end user's workflow or mental model, or their pain points and product opportunities. Every finding stays traceable to what a participant actually said.

I pair qualitative depth with quantitative evidence: surveys, ranking and rating exercises, and mention counts across studies show how widespread a need is, not just that someone raised it. The result is research a team can act on and defend, from a single feature decision to a multi-year roadmap.